课程描述

Bachelor of Science in 数据科学

For a full list of all courses offered by the Department of Mathematics, visit the 课程目录.

学生 will learn the basic programming techniques needed to create simple scripts/program to automate and perform simple computer operations. 学生 will learn the skills needed to implement algorithms to solve computing problems using select scripting languages. Course will review common structures between scripting languages to include Python, Ruby, PERL, PowerShell, SQL和BASH. Topics will include basic performance optimization and security practices in developing scripts and programs using Python.

先决条件: 2320年独联体

Learn the basic programming techniques needed to create simple scripts/program to automate and perform simple computer operations. Learn the skills needed to implement algorithms to solve computing problems using select scripting languages. Course will review common structures between scripting languages to include Python, Ruby, PERL, PowerShell, SQL和BASH. Topics will include basic performance optimization and security practices in developing scripts and programs using Python.

先决条件: CIS 2330或CSEC 2330

A course designed to give students an introduction to the fundamentals of data science. 学生 will learn the essential skills necessary for work in data science. This course assumes no previous computer programming experience, and it assumes no prior knowledge in statistics.

先决条件: 数学1304

This three-hour course provides a broad introduction to machine learning and statistical pattern recognition, with a focus on mathematical foundations and programming techniques. 学生 may learn to use a variety of software packages, 比如R, Python, MATLAB, 一款统计软件, 无条件转移指令, 或情景应用程序. The course will also discuss application of Machine Learning to Data Mining.

先决条件: 数学3332
This three-hour course provides a broad introduction to Deep Learning, with a focus on mathematical foundations and programming techniques. 到学期末, students will understand how to build neural networks, and learn how to lead successful machine learning projects. 学生 may learn to use a variety of software packages, 比如R, Python, MATLAB, 一款统计软件, 无条件转移指令, 或情景应用程序.

先决条件: 数学2340和数据4365
This course seeks to aid development of academic maturity required for students majoring in 数据科学. As such, it is intended for students who have completed the major requirements. 学生 who complete this course will have the fundamental tools to begin solving real-world problems. The course is the culmination of preparation for work as a Data Scientist and requires permission of the instructor.

这是一个三小时的课程, 它包括函数, 限制, 衍生品, 不定式, 和积分, exponential and logarithmic 功能; inverse trigonometric 功能, 和应用程序.

先决条件:  数学1311

数学课程描述

This course includes techniques of integration, 集成的应用, 反常积分, infinite series and calculus using polar and parametric curves.

先决条件: 数学2312

This course covers vector spaces, linear transformations and matrices.

先决条件: 数学1304 or 数学1311 or 数学2312 or 数学2313 or MATH 2314

数学课程描述

This is an introductory course in C programming for mathematics, sciences and engineering majors. 主题包括:数据类型, 以及相关操作, 浮动的错误, 输入/输出, 控制结构, 功能, 数组, 数据结构, 文件和字符串处理. Program design, debugging techniques and good programming practices will also be discussed. Programming exercises and projects will emphasize problems 和应用程序 in mathematics, sciences and engineering fields.

本课程涵盖向量, differential calculus of 功能 of several variables, 多重积分, 和应用程序.

先决条件: 数学2313

This three-hour course covers probability, 统计学基础, 随机变量函数, discrete and continuous distributions, moments and moment-generating 功能. It is part one of a two-course sequence with 数学3332, Foundations of Statistical Inference. 学生 should have either completed 数学2313, 微积分二世, or be enrolled in 数学2313 in the same semester with this course.

先决条件: 数学2313

This three-hour course covers techniques of statistical inference including sampling theory, 评估程序, 假设检验, and method of maximum likelihood. It is part two of a two-course sequence with 数学3331 Foundations of Probability and Statistics.

先决条件: 数学3331

 

This three-hour course covers the theory and basic applications of regression analysis. Topics covered include simple linear regression, 多元回归, 模型选择过程, 基本的非线性模型, such as logistic and probit regression. The course also covers the use of software packages R, 一款统计软件, and SAS.

先决条件: 数学3332